Scientists at Google and its Alphabet sister company Verily Life Sciences found that deep-learning algorithms analysing photographs of a retina were able to predict cardiovascular risk.

The research was published in the journal Nature Biomedical Engineering.

The status of blood vessels in the eye was shown to be an accurate predictor of smoking habits, blood pressure, age, gender, prior heart attacks and ethnicity, all factors in predicting cardiovascular-related diseases.

The algorithm was trained on data from 284,335 patients and used to predict cardiovascular risk factors in two independent datasets of 12,026 and 999 patients with “surprisingly high accuracy”, according to Google Brain Team product manager Lily Peng.

"Our algorithm could pick out the patient who had the CV (cardiovascular) event 70 per cent of the time. This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol," wrote Peng.

These other risk calculators include a patient’s history and blood samples. Researchers said that sometimes key information such as cholesterol levels are missing from these predictions.

So could these retinal image scans offer a fast, cheap and non-invasive alternative?

Head of cardiovascular health innovations at Verily Michael McConnell said: "More work must be done to develop and validate these findings on larger patient cohorts before this can arrive in a clinical setting.”